The connective tissue growth factor (CTGF) gene is known to be important in cell growth, bone and cartilage differentiation, and wound healing. The molecular mechanisms and exact role that CTGF plays in these processes are still unclear. A greater understanding of the evolutionary history of this gene may help identify regions of the gene important at the molecular level of wound healing. Aligning CTGF sequences from 19 different species allowed for identification of regions in the CTGF gene that are conserved across evolutionary history. We have matched single nucleotide polymorphisms (SNPs) detected by sequencing individuals at Plymouth State to these highly conserved regions. Surprisingly, we have identified 18 SNPs in humans within regions of the gene that are highly conserved. In addition, an excess of SNPs that cause amino acid changes in these regions suggests there is positive selective pressure on this gene in humans. Using a comparative protein modeling utility, RaptorX, we have identified SNPs that have significant impact on the protein structure of CTGF. Understanding evolutionary pressures on CTGF and identifying significantly different variants among humans can help increase understanding of this gene and its involvement in healing.

1. Methods
Introduction
Acknowledgements
We would like to thank Plymouth State University, the PSU Research Advisory
Council, the PSU Student Research Advisory Council, and the New Hampshire Idea
Network of Biological Research Excellence for funding support.
We would also like to thank Lauren Oakes, Ethan Johnson, Evyn Grimes, Kim
Jesseman, Alycia Wiggins, Ellen Rounds, Harlie Shaul, Kate-Lyn Skribiski, Chris Gonzalez,
Justin Provazza, John Rollins, and the University of New Hampshire Hubbard Center for
Genome Studies DNA core, and Dartmouth College Molecular Biology Shared
Resources Lab for their contributions.
Conclusions
Future Directions
Department of Biological Sciences and Biotechnology Program at Plymouth State University in Plymouth, NH
References
1. Chen, Chih-Chiun and Lau, Lester F. 2010. Functions and Mechanisms of Action of CCN Matricellular
Proteins. Int J Biochem Cell Biol. Apr 2009; 41(4): 771–783.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2668982/
2. Doherty, H. The Role of Quantitative Variations in Connective Tissue Growth Factor Gene Expression in
Cardiac Hypertrophy and Fibrosis. Chapel Hill. (2010):11-12
3. Gupta, Sunil, et al. Connective tissue growth factor: potential role in glomerulosclerosis and tubulointerstitial
fibrosis. Kidney international 58.4 (2000): 1389-1399.
4. Ito, Yasuhiko, et al. Expression of connective tissue growth factor in human renal fibrosis. Kidney
international 53.4 (1998): 853-861.
5. Khan, Razi, and Richard Sheppard. "Fibrosis in heart disease: understanding the role of transforming growth
factor-β1 in cardiomyopathy, valvular disease and arrhythmia." Immunology 1 (2006): n. pag. NCBI. Web. 26
Mar. 2013.
6. Wilson, Peter WF, et al. Prediction of coronary heart disease using risk factor categories. Circulation 97.18
(1998): 1837-1847.
7. Ensembl Genome Browser. (n.d.). Retrieved from http://www.ensembl.org/index.html
8. Chromas Lite (Version 2.1.1) [Computer software]. (n.d.).
9. A QIAGEN Company. (2014, February). CLC Genomic Workbench 7 (Version 7) [Computer software].
Retrieved from http://www.clcbio.com/products/clc-genomics-workbench/#latest-improvements
10. Morten Källberg, Haipeng Wang, Sheng Wang, Jian Peng, Zhiyong Wang, Hui Lu & Jinbo Xu. Template-based
protein structure modeling using the RaptorX web server. Nature Protocols 7, 1511–1522, 2012.
11. Clustal Omega [Computer software]. (n.d.). Retrieved from http://www.ebi.ac.uk/Tools/msa/clustalo/
12. "National Center for Biotechnology Information." National Center for Biotechnology Information. U.S.
National Library of Medicine, n.d. Web. <http://www.ncbi.nlm.nih.gov/>.
13. ab1 Peak Reporter [Computer software]. (n.d.). Retrieved from
https://apps.lifetechnologies.com/ab1peakreporter/
14. Butler, JM. Forensic DNA Typing: Biology, Technology, and Genetics of STR Markers. Academic Press.
(2005):156.
15. Adzhubei IA, et al. A method and server for predicting damaging missense mutations. Nat Methds
7(4):248-249, 2010.
16. Huttley, G., Easteal, S., Southey, M., Tesoriero, A., Giles, G., McCredie, M., Hopper, J., Venter, D., the
Australian Breast Cancer Family Study. 2000. Adaptive evolution of the tumour suppressor BRAC1 in humans
and chimpanzees. Nature Genetics, 25: 410-413.
17. Kong, X., Wang, X., Gan, X., Li, J., and He, S. 2008. Molecular evolution of connective tissue growth factor in
Cyprinidae (Teleosteri: Cypriniformes). Progress in Natural Science, 18: 155-160.
18. Koichiro Tamura, Glen Stecher, Daniel Peterson, Alan Filipski, and Sudhir Kumar (2013) MEGA6: Molecular
Evolutionary Genetics Analysis version 6.0. Molecular Biology and Evolution:30 2725-2729.
19. Yang, Z. and Nielsen, R. 2002. Codon-Substitution Models for Detecting Molecular Adaptation at Individual
Sites Along Specific Lineages. Molecular Biology and Evolution, 19(6): 908-917.
20. Molecular graphics and analyses were performed with the UCSF Chimera package. Chimera is developed by
the Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco
(supported by NIGMS P41-GM103311).
Selection Across Primate Species
An Evolutionary and Structural Analysis of the Connective Tissue Growth Factor Gene
Ashley E. Kennedy, Joel R. Dufour and Heather E. Doherty
Region Published Data Data from PSU
Population
Whole Gene 2.1 2.7
Exon 1 0 -
Exon 2 1.7 -
Exon 3 1.8 2.0
Exon 4 2.8 4.0
Exon 5 2.4 2.6
A
B
Alignments and Trees
C
Connective tissue growth factor (CTGF) is an essential protein involved in
development, skeletogenesis, and wound healing. Like any other gene, the CTGF
gene is subject to variation between individuals, and some of this variation is
due to single nucleotide polymorphisms (SNPs). SNPs are single base variations
in a gene sequence seen between individuals. Several single nucleotide pair
changes have been identified in CTGF through genetic sequencing of samples
from volunteers at Plymouth State University. Published and novel SNPs have
been identified, both of which have the potential to alter the structure and
function of the CTGF protein. Despite CTGF’s importance to human health, little
is known about its evolutionary history or the impacts of genetic variation on its
structure and function.
The goals of this research include analyzing the evolutionary history of the
CTGF gene, identifying the types of selective pressures affecting CTGF, and
understanding the impact of human sequence variations on the CTGF protein
structure. Alignments were used to compare CTGF gene sequences between
species and build phylogenetic trees. Regions of the gene that have been highly
conserved throughout the evolutionary history of CTGF were also identified. To
help classify the types of selective pressures on the CTGF gene over its
evolutionary history in primates, the ratio of SNPs that cause an amino acid
change (nonsynonymous SNPs) to SNPs that do not cause an amino acid change
(synonymous SNPs) between species was calculated. Finally, a protein modeling
prediction program, was used to predict the effects of SNPs on the structure of
the CTGF protein. By comparing the sequence of interest to similar sequences of
already-known structure, the utility provides predictions of protein structure.
These predictions were adequate for the identification of nonsynonymous
mutations that may alter protein domains of CTGF. Future directions of this
research include examining the selective pressure put on CTGF across a wider
range of species as well as introducing SNPs that may have significant impacts on
CTGF function into tissue culture cells to observe their phenotypic impacts.
Alignments, Tree Assembly, and Identification of Conserved Regions
The CTGF cDNA sequences for 19 different species were obtained from
Ensembl genome browser (http://www.ensembl.org/index.html). These
sequences were aligned using MEGA6,
(http://www.megasoftware.net/mega.php), and a maximum likelihood tree was
assembled from this alignment. Bases that were 100% conserved across these
19 species were noted, and SNPs that were detected in our Plymouth State
population at these base locations were documented.
Primate Tree Assembly and Selection Analysis
The CTGF cDNA sequences for 9 primates were obtained from Ensembl
genome browser. These sequences were aligned using MEGA6 and a maximum
likelihood tree was constructed. The bootstrap consensus version of this tree
was used for selection analysis. Alignments were implemented to compare CTGF
sequences at evolutionary branches, and variations that did (dN ) and did not
(dS) cause amino acid changes were tallied. The dN/dS or ω value for each node
was calculated by dividing the number of nonsynonymous variations by the
number of synonymous variations. The resulting value ω represents the ratio of
nonsynonymous to synonymous SNPs for each branch and is called the selection
coefficient. A ω<1 indicates negative selection on a gene, suggesting variations
in the gene sequence that alter the protein sequence are selected against. A
ω=1 indicates neutral selection, suggesting that there is no selective pressure
for or against variations that change the amino acid sequence. A ω>1 indicates
positive selection on that gene, suggesting that there is selective pressure to
preserve variations that have altered the amino acid sequence.
RaptorX Modeling And Amino Acid Analysis
Nucleic acid sequences of the CTGF gene obtained from the Plymouth State
population were translated into amino acid sequences using CLC Genomics
Workbench (Qiagen), and individual exons (3, 4, and 5) were submitted to the
RaptorX prediction server
(http://raptorx.uchicago.edu/StructurePrediction/predict) for analysis. Only
sequences with SNPs that were found in our population at a frequency above
1.8% were submitted for analysis. Upon receipt of the structures, sequences
were sorted into categories based on their predicted probability of causing
structural changes. Chimera 1.9 (http://www.cgl.ucsf.edu/chimera) was used to
create diagrams illustrating structural changes.
• Exons 1 and 2 are less variable than exon 5 across CTGF’s
evolutionary history and therefore may be more conserved
• New variants in exon 5 of humans may provide selective
advantages
• Positive selection in humans suggests evolutionary
pressure to alter CTGF
• Protein modeling can be used to identify SNPs that alter
the structure of CTGF
• Introduce SNPs of interest into tissue culture to observe
phenotypic effects
• Compare selection coefficients across wider range of
species using computer models that more accurately
estimate selection coefficients
• Consider other factors that may alter selection coefficients
Figure 1: Alignments and Trees – (A+B) Alignments of the CTGF gene for 19 different
species. Bases that were identified as 100% conserved across different species are
indicated by * above the alignment. (A) Alignment of a subset of exon 1. (B) Alignment
of a subset of exon 5. (C) Phylogenetic tree depicting CTGF homologues across 19
species.
Results & Discussion: CTGF homologues are found in all vertebrate species. Say
something about number of conserved bases. Alignments demonstrated that exons 1
and 2 are much less conserved across evolutionary history than exon 5. 18
nonsynonymous SNPs in the human CTGF gene were detected in the Plymouth State
population at 100% conserved bases. These SNPs were mainly found in exon 5, a region
that has been highly conserved for a long evolutionary time period. Increased variation
in the human CTGF gene is detected compared to other species, suggesting that there
may have been pressure to alter CTGF across human evolution.
Figure 2: Selective Pressures for CTGF Across Primate Species - (A) Alignment of CTGF
in 9 primate species. (B) Selection coefficient for each evolutionary branch within
primate species on a tree depicting lineage. (C) Selection coefficients for individual
exons of the human CTGF gene calculated using published data or data from the
Plymouth State University sample population.
Results & Discussion: Selection coefficients (ω) >1 are present at the divergence of the
prosimian linages from the rest of the higher primates indicating selective pressure in
favor of certain changes to the CTGF protein during this evolutionary point. Many of
the remaining branches show ω<1 indicating selective pressure against altered CTGF
protein sequence. In divergence of humans from chimpanzees and gorillas, ω = 1
indicates little selective pressure to alter the CTGF protein. During human evolution, ω
= 2.1 suggests certain alterations to the CTGF protein during human evolution were
advantageous and therefore conserved. Overall, the selection coefficients from
published data were similar to our data, except in exon 4 (likely due to the small
number of variants in our sample). Exons 2-5 have ω >1, and exons 4 and 5 exhibit the
highest selection coefficients, which is surprising as they occur in the most highly
conserved region across species.
Protein Structure Predictions
A B C D
Figure 3: Protein Structure Predictions – (A) Predicted reference structure from published CTGF exon 5 sequence with regions where the three most common SNP-related amino acid
changes occur highlighted in red (D332N), magenta (C284W), and blue (T294P). (B) Prediction for amino acid (A.A.) change D332N, PSU population frequency = 18.1%. (C) Prediction for
A.A. change C284W, PSU population frequency = 8.6%. (D) Prediction for A.A. change T299P, PSU population frequency = 6.5%. A visualization of each change is inset.
Results & Discussion: The first SNP, (B), shows a minor change in physical conformation, but is relatively common and worth further investigation in tissue culture. The amino acid change
in (C) represents a large difference in side-chain structure, and a potential disruption of disulfide bonding in the protein. This, combined with its high frequency, gives this change a
strong potential for phenotypic variation. Finally, in (D), a break in the ribbon structure suggests local disruption of the protein structure. This may lead to a changes in protein activity,
and therefore phenotypic change.
A B
C
2.1